Energy Storage Systems (ESS)s have become widely pervasive in several sectors, both in the civil and in the industrial engineering fields. Among the several applications, the most critical ones regard the storing of energy in the future Smart Grids and microgrids, and the power sourcing for Electric and Hybrid Vehicles. In this context, the management of the ESS represents a crucial task in order to guarantee efficient, effective and robust energy storing. In order to achieve a safe and reliable usage of ESSs, it is important to synthesize suitable models capable to predict the cell behavior in order to avoid damages, to estimate the State of Charge (SoC) and the State of Health (SoH), and to perform the cells equalization. Moreover, the design of efficient and effective algorithms for optimal energy flows routing in Smart Girds and microgrids is a challenging task, especially in presence of ESSs. Computational intelligence techniques represent a powerful approach to face the above-mentioned tasks, allowing to deal with the strong nonlinear and dynamic behavior of electrochemical cells, as well as to design Energy Management Systems (EMS) able to cope with nonlinear and time variant systems, such as microgrids and Smart Grids, especially in presence of stochastic renewable energy sources.

Topics of interest include (but are not limited to) applications of Computational Intelligence techniques (Neural networks and Machine Learning, Evolutionary Optimization and Fuzzy Systems) to the following problems:

Prof. Fabio Massimo Frattale Mascioli received his MS and PhD in Information and Communication Engineering in 1989 and 1995, from the University "La Sapienza" of Rome. In 1996, he joined the DIET Department of the University "La Sapienza" of Rome as Assistant Professor. He was promoted to Associate Professor of Circuit Theory in 2000 and to Full Professor in 2011. His research interest mainly regards neural networks and neuro-fuzzy systems and their applications to clustering, classification and function approximation problems, circuit modeling for vibration damping, energy conversion systems and electric and hybrid vehicles. He is author or co-author of more than 150 papers. Since 2007, he serves as scientific director of the `Polo per la Mobilità Sostenibile' (POMOS) Laboratories, DIET Department.

Antonello Rizzi

Antonello Rizzi received the Ph.D. in Information and Communication Engineering in 2000, from the University of Rome “La Sapienza”. In September 2000, he joined the INFO-COM Dpt., as an Assistant Professor. Since July 2010 he joined the “Information Engineering, Electronics and Telecommunications” Dpt. (DIET), in the same University. His major research interests are in the area of Soft Computing, Pattern Recognition and Computational Intelligence, including supervised and unsupervised data driven modeling techniques, neural networks, fuzzy systems and evolutionary algorithms. His research activity concerns the design of automatic modeling systems, focusing on classification, clustering, function approximation and prediction problems. Currently, he is working on different research topics and projects, such as Granular Computing, Data Mining and Knowledge Discovery, Content Based Retrieval Systems, classification and clustering systems for structured patterns, graph and sequence matching, agent-based clustering, smart grids and micro-grids modeling and control, intelligent systems for sustainable mobility, battery management systems. Since 2008, he serves as the scientific coordinator and technical director of the R&D activities in the "Intelligent Systems Laboratory" within the Research and Technology Transfer Center for Sustainable Mobility of Lazio Region. He is the scientific coordinator of the "Computational Intelligence and Pervasive Systems" Lab at DIET. Dr. Rizzi (co-)authored more than 140 international journal/conference papers and book chapters. He is a member of IEEE.

Maurizio Paschero

Maurizio Paschero is a post-doctoral research associate at the Information Engineering, Electronics and Telecommunications Department of the University of Rome "La Sapienza" since September 2008, where he works in the Polo per la Mobilià Sostenibile (POMOS) Laboratories.
He received his M.S in Electronic Engineering 2003 and the Ph.D in Information and Communication Engineering in 2006 from the University "La Sapienza" of Rome and the Ph.D in Mechanical Engineering in 2008 from Virginia Polytechnic Institute and State University.
His major fields of interest include Soft computing, Smart Grids, multi-physic circuital modeling, intelligent signal processing, and battery modeling. He is author or more than 40 scientific publications on international journals and conferences.

Authors’ Information
Papers submitted to this Special Session are reviewed according to the same rules as the submissions to the regular sessions of WCCI 2018.
Authors who submit papers to this session are invited to mention it in the form during the submission.
Submissions to Regular and Special Sessions follow identical format, instructions, deadlines and procedures of the other papers.

Multiobjective Evolutionary Computation has been a major research topic in the field of evolutionary computation for many years. It has been generally accepted that combination of evolutionary algorithms and traditional optimization methods should be a next generation multiobjective optimization solver. Decomposition methods have been well used and studied in traditional multiobjective optimization. In this talk, I will describe MOEA/D algorithmic framework. MOEA/D decomposes a multiobjective problem into a number of subtasks, and then solves them in a collaborative manner. MOEA/D provides a very natural bridge between multiobjective evolutionary algorithms and traditional decomposition methods. It has been a commonly used evolutionary algorithmic framework in recent years. I will explain the basic ideas behind MOEA/D and some recent developments. I will also outline some possible research issues in multiobjective evolutionary computation.

Bio

Prof. Qingfu Zhang is a Professor at the Department of Computer Science, City University of Hong Kong, Hong Kong. His main research interests include evolutionary computation, optimization, neural networks, data analysis, and their applications. He is currently leading the Metaheuristic Optimization Research Group in City University of Hong Kong. MOEA/D, a multiobjective optimization algorithmic framework, developed in his group, is one of the most widely used and researched multiobjective evolutionary algorithmic framework.

Dr. Zhang is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions on Cybernetics. He is also an Editorial Board Member of three other international journals. He was awarded the 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is a 2016 and 2017 highly cited researcher in Computer Science (Clarivate Analytics) and an IEEE fellow. He was selected in the 1000 talents program in China in 2015. He was a Changjiang visiting chair professor with Xidian University, China from 2011 to 2014.

Friday, 17 November 2017

Jul 8-13, 2018, Rio de Janeiro, BRAZIL

We proposed a special
session on “Evolutionary Computation in Healthcare Industry” in IEEE IEEE
Congress on Evolutionary Computation 2018 (CEC 2018). Please consider to contribute
to and/or forward to the appropriate groups the following opportunity to publish
original research articles in CEC 2018.

Worldwide, the
healthcare industry would continue to thrive and grow, because diagnosis,
treatment, disease prevention, medicine, and service affect the mortal rates
and life quality of human beings. Two key issues of the modern healthcare
industry are improving healthcare quality as well as reducing economic and
human costs. The problems in the healthcare industry can be formulated as
scheduling, planning, predicting, and optimization problems, where evolutionary
computation methods can play an important role. Although evolutionary
computation has been applied to scheduling and planning for trauma system and
pharmaceutical manufacturing, other problems in the healthcare industry like
decision making in computer-aided diagnosis and predicting for disease
prevention have not properly formulated for evolutionary computation techniques,
and many evolutionary computation techniques are not well-known to the
healthcare community. This special session aims to promote the research on
evolutionary computation methods for their application to the healthcare
industry.

Scope and Topics:

The topics of
this special session include but are not limited to the following topics:

Machine learning and search algorithms play an imperative role in solving real world problems in industry and business sectors. Systems employing these techniques have contributed to many facets of industry including data mining, transportation, health systems, computer vision, computer security, robotics, software engineering and scheduling amongst others. These systems employ one or more techniques such as neural networks, fuzzy logic, evolutionary algorithms, multi-agent approaches and rule-based systems. Implementation of these techniques require a number of design decisions to be made, e.g. what architecture to use, what parameter values to use, and derivation of problem specific operators. It may also be necessary to employ a hybrid system combining techniques to solve a problem which introduces additional decisions such as which techniques to use and how to combine these techniques. This makes the development of computational systems time consuming, requiring many person-hours. Consequently, there have been a number of initiatives to automate these processes using computational intelligence.

There has been a fair amount of research into parameter tuning and control. The field of auto-ML aims to automate the design of machine learning algorithms so as to produce off-the-shelf machine learning techniques. Attempts to automate neural network architecture design has led to the field of neuroevolution. Research in this area has also been directed at inducing fuzzy functions, rule-based systems and multi-agent architectures. Hyper-heuristics, which were initially aimed at providing generalized solutions to combinatorial optimization problems, are shown to be effective in the automated design of search techniques. Evolutionary algorithms such as genetic programming and genetic algorithms have made a valuable contribution to this field. The aim of this special session is to examine and promote recent developments in the field and future directions including the challenges and how these can be overcome.

Special session papers are treated the same as regular papers and must be submitted via the WCCI 2018 submission website. When submitting choose the " Computational Intelligence for the Automated Design of Machine Learning and Search " special session from the "Main Research Topic" list.

Monday, 13 November 2017

SpeakerDr. Veronique Ventos, Associate Professor, University Paris Saclay
AbstractGames have always been an excellent field of experimentation for the nascent techniques in computer science and in different areas of Artificial Intelligence including Machine Learning. Despite their complexity, game problems are much easier to understand and to model than real life problems. Systems initially designed for games are then used in the context of real applications. In the last decades, designs of champion-level systems dedicated to a game (game AI) were considered as milestones of computer science and AI.
The first part of the webinar is devoted to the presentation of the different aspects of bridge and of various challenges inherent to it.
In a second part, we will present our work concerning the optimization of the AI Wbridge5 developed by Yves Costel. This work is based on a recent seed methodology which optimizes the quality of Monte-Carlo simulations and which has been defined and validated in other games. The Wbridge5 version boosted with this method won the World Computer-Bridge Championship twice, in September 2016 and in August 2017.
Finally, the last part is about various ongoing works related to the design of a hybrid architecture entirely dedicated to bridge using recent numeric and symbolic Machine Learning modules.
BiographyPhD in Artificial Intelligence (Knowledge Representation and Machine Learning) in 1997. Associate professor at University Paris Saclay, France since 1998. Before joining in 2015 the group A&O in the interplay of Machine Learning and Optimization, she worked in the group LaHDAK (Large-scale Heterogeneous DAta and Knowledge) at Laboratory of Computer Science (LRI). She started playing bridge in 2004 and is now 59th French woman player out of 48644 players. In 2015, she set up the AlphaBridge project combining her two passions. AlphaBridge is dedicated to solve the game of bridge by defining a hybrid architecture including recent numeric and symbolic ML modules.Register at: https://register.gotowebinar.com/register/6958887543473419777

Call for Papers

On behalf of the IEEE WCCI 2018 Organizing Committee, it is our great pleasure to invite you to the bi-annual IEEE World Congress on Computational Intelligence (IEEE WCCI), which is the largest technical event in the field of computational intelligence. The IEEE WCCI 2018 will host three conferences: The 2018 International Joint Conference on Neural Networks (IJCNN 2018 – co-sponsored by International Neural Network Society – INNS), the 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2018), and the 2018 IEEE Congress on Evolutionary Computation (IEEE CEC 2018) under one roof. It encourages cross-fertilization of ideas among the three big areas and provides a forum for intellectuals from all over the world to discuss and present their research findings on computational intelligence.

IEEE WCCI 2018 will be held at the Windsor Convention Centre, Rio de Janeiro, Brazil. Rio de Janeiro is one of the most attractive cities in South America, with the largest urban forest in the world, beautiful bays, lagoons and 90 kms of beaches and mountains. Known as one of the most beautiful cities in the World, Rio de Janeiro is the first city to receive the certificate of World Heritage for its Cultural Landscape. This unprecedented title was recently conferred by the United Nations Educational, Cultural and Scientific Organization (UNESCO).

Rio de Janeiro is easily accessible from all over the world, with direct flights from major cities in North America, Europe, Africa and Middle East. It is also a one stop away from Asia and Australia. The venue, The Windsor Barra Complex, features a brand new Convention Center and three different categories hotels, in the fastest growing region in Rio de Janeiro, with walking distance from a great choice of restaurants and shopping centers.

IEEE Computational Intelligence Society has maintained its position as a leader of journals in computational intelligence. CIS journals sustained their status as premier scholarly publications, earning high rankings in the Journal Citation Report by Thomson Reuters.

IEEE Transactions on Neural Networks and Learning Systems (IF: 4.854)

IEEE Transactions on Fuzzy Systems (IF: 6.701)

IEEE Transactions on Evolutionary Computation (IF: 5.908)

IEEE Computational Intelligence Magazine (IF: 3.647)

List of topics:

IEEE CEC

Algorithms

Ant colony optimization

Artificial immune systems

Coevolutionary systems

Cultural algorithms

Differential evolution

Estimation of distribution algorithms

Evolutionary programming

Evolution strategies

Genetic algorithms

Genetic programming

Heuristics, metaheuristics and hyper-heuristics

Interactive evolutionary computation

Learning classifier systems

Memetic, multi-meme and hybrid algorithms

Molecular and quantum computing

Multi-objective evolutionary algorithms

Parallel and distributed algorithms

Particle swarm optimization

Theory and Implementation

Adaptive dynamic programming and reinforcement learning

Autonomous mental development

Coevolution and collective behavior

Convergence, scalability and complexity analysis

Evolutionary computation theory

Representation and operators

Self-adaptation in evolutionary computation

Optimization

Numerical optimization

Discrete and combinatorial optimization

Multiobjective optimization

Handling of Various Aspects

Large-scale problems

Preference handling

Evolutionary simulation-based optimization

Meta-modeling and surrogate models

Dynamic and uncertain environments

Constraint and uncertainty handling

Hybrid Systems of Computational Intelligence

Evolved neural networks

Evolutionary fuzzy systems

Evolved neuro-fuzzy systems

Related Areas and Applications

Art and music

Artificial ecology and artificial life

Autonomous mental and behavior development

Biometrics, bioinformatics and biomedical applications

Classification, clustering and data analysis

Data mining

Defense and cyber security

Evolutionary games and multi-agent systems

Evolvable hardware and software

Evolutionary Robotics

Engineering applications

Emergent technologies

Finance and economics

Games

Intelligent systems applications

Robotics

Real-world applications

Emerging areas

IJCNN

NEURAL NETWORK MODELS

Feedforward neural networks

Recurrent neural networks

Self-organizing maps

Radial basis function networks

Attractor neural networks and associative memory

Modular networks

Fuzzy neural networks

Spiking neural networks

Reservoir networks (echo-state networks, liquid-state machines, etc.)

Large-scale neural networks

Learning vector quantization

Deep neural networks

Randomized neural networks

Other topics in artificial neural networks

MACHINE LEARNING

Supervised learning

Unsupervised learning and clustering, (including PCA, and ICA)

Reinforcement learning and adaptive dynamic programming

Semi-supervised learning

Online learning

Probabilistic and information-theoretic methods

Support vector machines and kernel methods

EM algorithms

Mixture models, ensemble learning, and other meta-learning or committee algorithms